Dysfunctional and abnormal functional connectivity in the right anterior insula (rAI) may underlie the pathophysiology of depression episode in bipolar disorder (BD) and of major depressive disorder ...(MDD). In this study, we examined the dynamic functional connectivity (dFC) of the rAI of 30 patients with BD, 30 patients with MDD, and 30 healthy controls. In the functional separation of rAI, the right dorsal AI (rdAI) and ventral AI (rvAI) were defined as seed regions. Sliding-window correlation of rAI subregions was implemented to measure the variance of dFC. BD and MDD shared abnormality in dFC, such as the decreased dFC between the rvAI and right ventrolateral prefrontal cortex. Others were disorder-specific and included MDD-related increases in dFC between the rvAI and right precuneus, temporal pole, and left dorsolateral prefrontal cortex. This observation is in stark contrast to BD-related increases in the dFC between the rdAI and left inferior parietal lobule and right middle occipital gyrus. The abnormal dFC of rAI shared by BD and MDD supports the importance of rAI in the common pathophysiology of these disorders. Meanwhile, disorder-specific abnormalities that attribute to the dorsal and ventral divisions of rAI can be used as biomarkers to differentiate BD from MDD.
•The rAI was functionally segregated into the dorsal and ventral part.•BD and MDD were equally characterized by decreased dFC between rvAI and vlPFC.•MDD-specific abnormality was related to increased variability in rvAI-related internally-oriented system.•BD-specific abnormality was related to increased variability in rdAI-related externally-oriented system.•Disorder-specific abnormalities can be used as biomarkers to differentiate BD from MDD.
lWe explored the abnormal dynamic connectivity patterns among brain systems in GAD, and performed the anxiety symptom severity predictive analysis on the basis of the altered connectivity ...patterns.lThe abnormal dynamic connectivity patterns were found in the bilateral dorsomedial prefrontal cortex (dmPFC), left hippocampus, and the right postcentral gyrus. The abnormal dFCD variability of the left dmPFC was an important feature for anxiety symptom severity prediction.lThe dynamic functional abnormalities of GAD may deepen our understanding of the disease.
Numerous studies have revealed the abnormal static functional connectivity (FC) among different brain regions in patients with generalized anxiety disorder (GAD). However, little is known about the dynamic changes of FC in patients with GAD.
This study investigated the whole-brain dynamic changes of FC in patients with GAD by combining global FC density (FCD) and sliding window correlation analyses. The standard deviation of dynamic FCD (dFCD) was calculated to evaluate its temporal variability along time. Support vector regression was then employed to predict the symptom severity of patients based on abnormal dynamic connectivity patterns.
The abnormal dFCD variability between 81 GAD patients and 80 healthy controls showed that the patients had higher dFCD variability in the bilateral dorsomedial prefrontal cortex (dmPFC) and left hippocampus while lower dFCD variability in the right postcentral gyrus. The abnormal dFCD variability of the left dmPFC is an important feature for anxiety prediction.
The selection of sliding window length remains controversial, and most of our patients have been treated with medications. Future studies are expected to rule out the potential confounding effects from applying different parameters of the sliding window and recruiting large samples of medication-free patients.
The altered patterns of time-varying brain connectivity in the frontolimbic and sensorimotor areas may reflect abnormal dynamic neural communication between these regions and other regions of the brain, which may deepen our understanding of the disease.
Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among ...brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.
The amygdala plays a key role in emotion processing. Its functional connectivity with other brain regions has been extensively demonstrated to be associated with extraversion and neuroticism. ...However, how the amygdala affects other regions and is affected by others within these connectivity patterns associated with extraversion and neuroticism remains unclear. To address this issue, we investigated the effective connectivity of the amygdala using Granger causality analysis on the resting-state functional magnetic resonance imaging data of 70 participants. Results showed that extraversion was positively correlated with the influence from the right inferior occipital gyrus (IOG) to the left amygdala, and from the bilateral IOG to the right amygdala; such result may represent the neural correlates of social interactions in extraverts. Conversely, neuroticism was associated with an increased influence from right amygdala to right middle frontal gyrus and a decreased influence from right precuneus to right amygdala. This influence might affect the modulations of cognitive regulation function and self-referential processes in neurotic individuals. These findings highlight the importance of the causal influences of amygdala in explaining the individual differences in extraversion and neuroticism, and offer further insights into the specific neural networks underlying personality.
Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the ...authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.
In recent years, the psychological problems of college students have attracted extensive attention. It is of great practical significance to timely and arcuately intervene in college sports majors ...faced with psychological crisis (PC). However, the existing studies mainly analyze the mood and psychological state at a certain moment, but rarely track the psychological health state of different types of college students. This paper proposes a way to intervene and predict the PC of college sports majors based on big data analysis. Firstly, the massive evaluation data were collected from a psychological census database on PC of college sports majors and subjected to data mining. Besides, a PC evaluation model was established based on the decision tree (DT) algorithm. Next, the behavior big data of college sports majors in social network were fully utilized, and a PC intervention and prediction method was developed based on social network readme texts. Further, the authors extracted features from readme texts, evaluated the level of PC risk, and analyzed the longitudinal features. Finally, the proposed model was proved valid through experiments. This paper effectively applies new technologies to the data mining of the typical behaviors of college sports majors, and thereby realizes accurate PC warning. The research results are of great significance to improving the psychological health of college students.
Finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. In view of drive and response concept, the sufficient conditions to ensure finite ...time synchronization issue of inertial memristive neural networks is given. Based on Lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. Based on Lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. Finally, illustrative example is given to illustrate the effectiveness of theoretical results.
Summary
Objective
Noncoding alleles of the fat mass and obesity‐associated (FTO) gene have been associated with obesity risk, yet the underlying mechanisms remain unknown. Risk allele carriers show ...alterations in brain structure and function, but previous studies have not disassociated the effects of genotype from those of body mass index (BMI).
Methods
Differences in brain structure and function were examined in children without obesity grouped by their number of copies (0,1,2) of the FTO obesity‐risk single‐nucleotide polymorphism (SNP) rs1421085. One hundred five 5‐ to 10‐year‐olds (5th–95th percentile body fat) were eligible to participate. Usable scans were obtained from 93 participants (15 CC homozygous risk, 31 CT heterozygous and 47 TT homozygous low risk).
Results
Homozygous C allele carriers (CCs) showed greater grey matter volume in the cerebellum and temporal fusiform gyrus. CCs also demonstrated increased bilateral cerebellar white matter fibre density and increased resting‐state functional connectivity between the bilateral cerebellum and regions in the frontotemporal cortices.
Conclusions
This is the first study to examine brain structure and function related to FTO alleles in young children not yet manifesting obesity. This study lends support to the notion that the cerebellum may be involved in FTO‐related risk for obesity, yet replication and further longitudinal study are required.
Electroconvulsive therapy (ECT) is an effective neuromodulatory treatment for major depressive disorder (MDD), especially for cases resistant to antidepressant drugs. While the precise mechanisms ...underlying ECT efficacy are still unclear, it is speculated that ECT modulates brain connectivity. The current study aimed to investigate the longitudinal effects of ECT on resting-state functional connectivity (FC) in MDD patients and test if baseline FC can be used to predict therapeutic response.
Resting-state functional magnetic resonance imaging data were collected at baseline and following ECT from 33 MDD patients. Whole-brain multi-voxel pattern analysis (MVPA) and region of interest-wise FC analysis were employed to fully investigate ECT effects on brain connectivity. Linear support vector regression was further utilized to predict the improvement in depressive symptoms based on baseline connectivity.
MVPA revealed a significant ECT effect on FC in the default mode network (DMN), central executive network (CEN), sensorimotor network (SMN), and cerebellar posterior lobe. The FCs within the DMN and between DMN and CEN were enhanced in patients after ECT, and the changed FC between the medial prefrontal cortex and ventrolateral prefrontal cortex was negatively correlated with depressive symptom improvement. Moreover, baseline FC within the DMN and between the DMN and CEN could effectively predict the improvement of depressive symptoms.
The findings suggest that the FCs within the DMN and between DMN and CEN may be critical therapeutic targets for effective antidepressant treatment as well as neuromarkers for predicting treatment response.
•Enhanced FCs within the DMN and between the DMN and CEN were found after ECT.•The changed FC between the MPFC and VLPFC was correlated with depressive symptoms improvement after ECT.•Baseline FCs within the DMN and between DMN and CEN could predict ECT treatment response.
Previous neuroimaging studies have mainly focused on alterations of static and dynamic functional connectivity in patients with generalized anxiety disorder (GAD). However, the characteristics of ...local brain activity over time in GAD are poorly understood. This study aimed to investigate the abnormal time‐varying local brain activity of GAD by using the amplitude of low‐frequency fluctuation (ALFF) method combined with sliding‐window approach. Group comparison results showed that compared with healthy controls (HCs), patients with GAD exhibited increased dynamic ALFF (dALFF) variability in widespread regions, including the bilateral dorsomedial prefrontal cortex, hippocampus, thalamus, striatum; and left orbital frontal gyrus, inferior parietal lobule, temporal pole, inferior temporal gyrus, and fusiform gyrus. The abnormal dALFF could be used to distinguish between patients with GAD and HCs. Increased dALFF variability values in the striatum were positively correlated with GAD symptom severity. These findings suggest that GAD patients are associated with abnormal temporal variability of local brain activity in regions implicated in executive, emotional, and social function. This study provides insight into the brain dysfunction of GAD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding neurophysiological mechanisms and potentially informing the diagnosis of GAD.
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